QuantumAnnealing¶
Quantum Annealing uses D-Wave’s purpose build Quantum Processing Units (QPU) to solve QUBO optimization problems with the help of the adiabatic theorem of quantum mechanics. This implementation first applies D-Wave’s minor embedding to map the provided problem onto the hardware graph of the desired sampler.
Compatible Backends¶
The QuantumAnnealing algorithm supports the following backends:
By default, QuantumAnnealing uses the DWaveQpu backend.
Initialization¶
The following section outlines the default configurations of QuantumAnnealing. You can also specify other compatible backends for the algorithm. When backend=None is specified, the default backend will be initialized automatically. In this case, if the backend requires a token, it will be taken from the environment variables.
from luna_quantum.solve.parameters.algorithms import QuantumAnnealing
algorithm = QuantumAnnealing(
backend=None,
anneal_offsets=None,
anneal_schedule=None,
annealing_time=None,
auto_scale=None,
fast_anneal=False,
flux_biases=None,
flux_drift_compensation=True,
h_gain_schedule=None,
initial_state=None,
max_answers=None,
num_reads=1,
programming_thermalization=None,
readout_thermalization=None,
reduce_intersample_correlation=False,
reinitialize_state=None
)
Parameter Details
For a complete overview of available parameters and their usage, see the QuantumAnnealing API Reference.
Usage¶
from luna_quantum import LunaSolve
LunaSolve.authenticate("<YOUR_LUNA_API_KEY>")
# Define your model and algorithm
model = ...
algorithm = ...
solve_job = algorithm.run(model, name="my-solve-job")
API Reference¶
Bases: QuantumAnnealingParams, LunaAlgorithm[DWaveQpu]
Quantum Annealing algorithm for physical quantum processors (QPUs).
Quantum Annealing is a metaheuristic that leverages quantum effects to find the ground state of a system, corresponding to the optimal solution of an optimization problem.
The process starts in a quantum superposition of all possible states, and gradually evolves the system according to a time-dependent Hamiltonian, exploiting quantum tunneling to potentially escape local minima more effectively than classical methods.
This implementation is specifically for D-Wave quantum annealers or similar hardware.
This class inherits all parameters from QuantumAnnealingParams, providing a complete set of controls for the quantum annealing process on hardware devices.
Attributes:
| Name | Type | Description |
|---|---|---|
anneal_offsets |
Any | None
|
Per-qubit time offsets for the annealing path. Default is None. |
anneal_schedule |
Any | None
|
Custom schedule for the annealing process. Default is None. |
annealing_time |
Any | None
|
Duration of the annealing process in microseconds. Default is None. |
auto_scale |
Any | None
|
Whether to automatically normalize the problem energy range. Default is None. |
fast_anneal |
bool
|
Use accelerated annealing protocol. Default is False. |
flux_biases |
Any | None
|
Custom flux bias offsets for each qubit. Default is None. |
flux_drift_compensation |
bool
|
Whether to compensate for drift in qubit flux over time. Default is True. |
h_gain_schedule |
Any | None
|
Schedule for h-gain during annealing. Default is None. |
initial_state |
Any | None
|
Starting state for the annealing process. Default is None. |
max_answers |
int | None
|
Maximum number of unique answer states to return. Default is None. |
num_reads |
int
|
Number of annealing cycles to perform. Default is 1. |
programming_thermalization |
float | None
|
Wait time after programming the QPU. Default is None. |
readout_thermalization |
float | None
|
Wait time after each anneal before reading results. Default is None. |
reduce_intersample_correlation |
bool
|
Whether to add delay between samples. Default is False. |
reinitialize_state |
bool | None
|
Whether to reset to a new initial state between reads. Default is None. |
anneal_schedule
class-attribute
instance-attribute
¶
annealing_time
class-attribute
instance-attribute
¶
backend
class-attribute
instance-attribute
¶
h_gain_schedule
class-attribute
instance-attribute
¶
max_answers
class-attribute
instance-attribute
¶
model_config
class-attribute
instance-attribute
¶
programming_thermalization
class-attribute
instance-attribute
¶
readout_thermalization
class-attribute
instance-attribute
¶
reduce_intersample_correlation
class-attribute
instance-attribute
¶
get_compatible_backends
classmethod
¶
get_compatible_backends() -> tuple[type[DWaveQpu], ...]
Check at runtime if the used backend is compatible with the solver.
Returns:
| Type | Description |
|---|---|
tuple[type[IBackend], ...]
|
True if the backend is compatible with the solver, False otherwise. |
get_default_backend
classmethod
¶
get_default_backend() -> DWaveQpu
Return the default backend implementation.
This property must be implemented by subclasses to provide the default backend instance to use when no specific backend is specified.
Returns:
| Type | Description |
|---|---|
IBackend
|
An instance of a class implementing the IBackend interface that serves as the default backend. |
run ¶
run(
model: Model | str,
name: str | None = None,
backend: BACKEND_TYPE | None = None,
client: LunaSolve | str | None = None,
*args: Any,
**kwargs: Any,
) -> SolveJob
Run the configured solver.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model
|
Model or str
|
The model to be optimized or solved. It could be an Model instance or a string identifier representing the model id. |
required |
name
|
str | None
|
If provided, the name of the job. Defaults to None. |
None
|
backend
|
BACKEND_TYPE | None
|
Backend to use for the solver. If not provided, the default backend is used. |
None
|
client
|
LunaSolve or str
|
The client interface used to interact with the backend services. If not provided, a default client will be used. |
None
|
*args
|
Any
|
Additional arguments that will be passed to the solver or client. |
()
|
**kwargs
|
Any
|
Additional keyword parameters for configuration or customization. |
{}
|
Returns:
| Type | Description |
|---|---|
SolveJob
|
The job object containing the information about the solve process. |